Overview

Dataset statistics

Number of variables25
Number of observations31734
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 MiB
Average record size in memory200.0 B

Variable types

DateTime2
Numeric14
Categorical9

Warnings

Tipo Cliente has constant value "Ualet puro" Constant
cant has constant value "1" Constant
Primer Nombre has a high cardinality: 3187 distinct values High cardinality
Ciudad has a high cardinality: 844 distinct values High cardinality
Id Modificado is highly correlated with Cod. Ocupacion High correlation
Cod_Ciudad_Nac. is highly correlated with Cod Dpto.Nacimto.High correlation
Cod Dpto.Nacimto. is highly correlated with Cod_Ciudad_Nac.High correlation
Cod. Ocupacion is highly correlated with Id ModificadoHigh correlation
Ingresos_mensuales is highly correlated with Otros_Ing_Mensuales and 1 other fieldsHigh correlation
Otros_Ing_Mensuales is highly correlated with Ingresos_mensuales and 1 other fieldsHigh correlation
Total_Activos is highly correlated with Ingresos_mensuales and 1 other fieldsHigh correlation
Transacciones is highly correlated with Transacciones Adiciones and 1 other fieldsHigh correlation
Transacciones Adiciones is highly correlated with Transacciones and 1 other fieldsHigh correlation
Transacciones Retiros is highly correlated with Transacciones and 1 other fieldsHigh correlation
Id Modificado is highly correlated with Codigo CIIU and 5 other fieldsHigh correlation
Codigo CIIU is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Cod_Ciudad_Nac. is highly correlated with Cod Dpto.Nacimto.High correlation
Cod Dpto.Nacimto. is highly correlated with Cod_Ciudad_Nac.High correlation
Cod. Ocupacion is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Ingresos_mensuales is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Egresos_mensuales is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Otros_Ing_Mensuales is highly correlated with Codigo CIIU and 5 other fieldsHigh correlation
Total_Activos is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Total_Pasivos is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Saldo is highly correlated with Transacciones AdicionesHigh correlation
Transacciones is highly correlated with Transacciones Adiciones and 1 other fieldsHigh correlation
Transacciones Adiciones is highly correlated with Saldo and 2 other fieldsHigh correlation
Transacciones Retiros is highly correlated with Transacciones and 1 other fieldsHigh correlation
Id Modificado is highly correlated with Codigo CIIU and 4 other fieldsHigh correlation
Codigo CIIU is highly correlated with Id Modificado and 6 other fieldsHigh correlation
Cod_Ciudad_Nac. is highly correlated with Cod Dpto.Nacimto.High correlation
Cod Dpto.Nacimto. is highly correlated with Cod_Ciudad_Nac.High correlation
Cod. Ocupacion is highly correlated with Id Modificado and 5 other fieldsHigh correlation
Ingresos_mensuales is highly correlated with Id Modificado and 5 other fieldsHigh correlation
Egresos_mensuales is highly correlated with Id Modificado and 5 other fieldsHigh correlation
Otros_Ing_Mensuales is highly correlated with Codigo CIIU and 1 other fieldsHigh correlation
Total_Activos is highly correlated with Id Modificado and 5 other fieldsHigh correlation
Total_Pasivos is highly correlated with Codigo CIIU and 5 other fieldsHigh correlation
Transacciones is highly correlated with Transacciones Adiciones and 1 other fieldsHigh correlation
Transacciones Adiciones is highly correlated with Transacciones and 1 other fieldsHigh correlation
Transacciones Retiros is highly correlated with Transacciones and 1 other fieldsHigh correlation
Riesgo is highly correlated with Id Modificado and 1 other fieldsHigh correlation
Transacciones Retiros is highly correlated with Transacciones Adiciones and 1 other fieldsHigh correlation
Otros_Ing_Mensuales is highly correlated with Ingresos_mensuales and 1 other fieldsHigh correlation
Id Modificado is highly correlated with Riesgo and 5 other fieldsHigh correlation
Perfil Riesgo is highly correlated with Riesgo and 1 other fieldsHigh correlation
Egresos_mensuales is highly correlated with Ingresos_mensualesHigh correlation
Cod_Ciudad_Nac. is highly correlated with Id Modificado and 2 other fieldsHigh correlation
Ocupacion is highly correlated with Id Modificado and 2 other fieldsHigh correlation
Ingresos_mensuales is highly correlated with Otros_Ing_Mensuales and 2 other fieldsHigh correlation
Total_Activos is highly correlated with Otros_Ing_Mensuales and 1 other fieldsHigh correlation
Codigo CIIU is highly correlated with CIIUHigh correlation
Transacciones Adiciones is highly correlated with Transacciones Retiros and 1 other fieldsHigh correlation
Departamento is highly correlated with Cod_Ciudad_Nac. and 1 other fieldsHigh correlation
Cod. Ocupacion is highly correlated with Id Modificado and 2 other fieldsHigh correlation
CIIU is highly correlated with Id Modificado and 3 other fieldsHigh correlation
Transacciones is highly correlated with Transacciones Retiros and 1 other fieldsHigh correlation
Cod Dpto.Nacimto. is highly correlated with Cod_Ciudad_Nac. and 1 other fieldsHigh correlation
Riesgo is highly correlated with Tipo Cliente and 2 other fieldsHigh correlation
Tipo Cliente is highly correlated with Riesgo and 5 other fieldsHigh correlation
Ocupacion is highly correlated with Tipo Cliente and 2 other fieldsHigh correlation
Perfil Riesgo is highly correlated with Riesgo and 2 other fieldsHigh correlation
cant is highly correlated with Riesgo and 5 other fieldsHigh correlation
Departamento is highly correlated with Tipo Cliente and 1 other fieldsHigh correlation
CIIU is highly correlated with Tipo Cliente and 2 other fieldsHigh correlation
Ingresos_mensuales is highly skewed (γ1 = 177.9249995) Skewed
Egresos_mensuales is highly skewed (γ1 = 178.0577763) Skewed
Otros_Ing_Mensuales is highly skewed (γ1 = 120.9799996) Skewed
Total_Activos is highly skewed (γ1 = 143.832213) Skewed
Total_Pasivos is highly skewed (γ1 = 139.5551282) Skewed
Saldo is highly skewed (γ1 = 66.44705337) Skewed
Id Modificado is uniformly distributed Uniform
Id Modificado has unique values Unique
Codigo CIIU has 20155 (63.5%) zeros Zeros
Cod Dpto.Nacimto. has 797 (2.5%) zeros Zeros
Cod. Ocupacion has 20158 (63.5%) zeros Zeros
Ingresos_mensuales has 20157 (63.5%) zeros Zeros
Egresos_mensuales has 20180 (63.6%) zeros Zeros
Otros_Ing_Mensuales has 26743 (84.3%) zeros Zeros
Total_Activos has 20176 (63.6%) zeros Zeros
Total_Pasivos has 22580 (71.2%) zeros Zeros
Saldo has 22013 (69.4%) zeros Zeros
Transacciones has 12588 (39.7%) zeros Zeros
Transacciones Adiciones has 12593 (39.7%) zeros Zeros
Transacciones Retiros has 18546 (58.4%) zeros Zeros

Reproduction

Analysis started2021-05-24 04:20:45.054945
Analysis finished2021-05-24 04:22:15.838439
Duration1 minute and 30.78 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Distinct1234
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Minimum2017-08-24 00:00:00
Maximum2021-03-25 00:00:00
2021-05-24T00:22:16.150094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-24T00:22:16.461931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Id Modificado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct31734
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15936.7554
Minimum1
Maximum31854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:17.128628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1606.65
Q17982.25
median15938.5
Q323890.75
95-th percentile30262.35
Maximum31854
Range31853
Interquartile range (IQR)15908.5

Descriptive statistics

Standard deviation9188.124201
Coefficient of variation (CV)0.5765366894
Kurtosis-1.199264991
Mean15936.7554
Median Absolute Deviation (MAD)7954.5
Skewness-0.0001147318079
Sum505736996
Variance84421626.33
MonotonicityStrictly increasing
2021-05-24T00:22:17.560421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
129791
 
< 0.1%
88811
 
< 0.1%
109281
 
< 0.1%
211511
 
< 0.1%
231981
 
< 0.1%
170531
 
< 0.1%
191001
 
< 0.1%
293391
 
< 0.1%
313861
 
< 0.1%
Other values (31724)31724
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
318541
< 0.1%
318531
< 0.1%
318521
< 0.1%
318511
< 0.1%
318501
< 0.1%
318491
< 0.1%
318481
< 0.1%
318471
< 0.1%
318461
< 0.1%
318451
< 0.1%

Tipo Cliente
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Ualet puro
31734 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters317340
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUalet puro
2nd rowUalet puro
3rd rowUalet puro
4th rowUalet puro
5th rowUalet puro

Common Values

ValueCountFrequency (%)
Ualet puro31734
100.0%

Length

2021-05-24T00:22:18.153404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:18.307847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
puro31734
50.0%
ualet31734
50.0%

Most occurring characters

ValueCountFrequency (%)
U31734
10.0%
a31734
10.0%
l31734
10.0%
e31734
10.0%
t31734
10.0%
31734
10.0%
p31734
10.0%
u31734
10.0%
r31734
10.0%
o31734
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter253872
80.0%
Uppercase Letter31734
 
10.0%
Space Separator31734
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a31734
12.5%
l31734
12.5%
e31734
12.5%
t31734
12.5%
p31734
12.5%
u31734
12.5%
r31734
12.5%
o31734
12.5%
Uppercase Letter
ValueCountFrequency (%)
U31734
100.0%
Space Separator
ValueCountFrequency (%)
31734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin285606
90.0%
Common31734
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U31734
11.1%
a31734
11.1%
l31734
11.1%
e31734
11.1%
t31734
11.1%
p31734
11.1%
u31734
11.1%
r31734
11.1%
o31734
11.1%
Common
ValueCountFrequency (%)
31734
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII317340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U31734
10.0%
a31734
10.0%
l31734
10.0%
e31734
10.0%
t31734
10.0%
31734
10.0%
p31734
10.0%
u31734
10.0%
r31734
10.0%
o31734
10.0%

Primer Nombre
Categorical

HIGH CARDINALITY

Distinct3187
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
JUAN
 
2367
CARLOS
 
901
LUIS
 
888
ANDRES
 
796
JOSE
 
706
Other values (3182)
26076 

Length

Max length13
Median length6
Mean length5.76353438
Min length1

Characters and Unicode

Total characters182900
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2019 ?
Unique (%)6.4%

Sample

1st rowDANIEL
2nd rowJESSICA
3rd rowDAVID
4th rowDIEGO
5th rowNICOLÁS

Common Values

ValueCountFrequency (%)
JUAN2367
 
7.5%
CARLOS901
 
2.8%
LUIS888
 
2.8%
ANDRES796
 
2.5%
JOSE706
 
2.2%
DANIEL677
 
2.1%
DIEGO634
 
2.0%
CRISTIAN523
 
1.6%
JORGE516
 
1.6%
DAVID508
 
1.6%
Other values (3177)23218
73.2%

Length

2021-05-24T00:22:18.753327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
juan2370
 
7.5%
carlos901
 
2.8%
luis888
 
2.8%
andres796
 
2.5%
jose706
 
2.2%
daniel678
 
2.1%
diego634
 
2.0%
cristian523
 
1.6%
jorge516
 
1.6%
david509
 
1.6%
Other values (3163)23213
73.1%

Most occurring characters

ValueCountFrequency (%)
A26919
14.7%
N18223
10.0%
I16600
9.1%
E15397
 
8.4%
R14116
 
7.7%
O13181
 
7.2%
L11072
 
6.1%
S11035
 
6.0%
D8382
 
4.6%
J7903
 
4.3%
Other values (38)40072
21.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter182866
> 99.9%
Other Punctuation9
 
< 0.1%
Modifier Symbol8
 
< 0.1%
Math Symbol6
 
< 0.1%
Decimal Number6
 
< 0.1%
Final Punctuation2
 
< 0.1%
Open Punctuation2
 
< 0.1%
Initial Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A26919
14.7%
N18223
10.0%
I16600
9.1%
E15397
 
8.4%
R14116
 
7.7%
O13181
 
7.2%
L11072
 
6.1%
S11035
 
6.0%
D8382
 
4.6%
J7903
 
4.3%
Other values (23)40038
21.9%
Other Punctuation
ValueCountFrequency (%)
'3
33.3%
*2
22.2%
"1
 
11.1%
\1
 
11.1%
/1
 
11.1%
:1
 
11.1%
Math Symbol
ValueCountFrequency (%)
~4
66.7%
|2
33.3%
Decimal Number
ValueCountFrequency (%)
15
83.3%
01
 
16.7%
Open Punctuation
ValueCountFrequency (%)
(1
50.0%
[1
50.0%
Modifier Symbol
ValueCountFrequency (%)
^8
100.0%
Final Punctuation
ValueCountFrequency (%)
»2
100.0%
Initial Punctuation
ValueCountFrequency (%)
«1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin182866
> 99.9%
Common34
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A26919
14.7%
N18223
10.0%
I16600
9.1%
E15397
 
8.4%
R14116
 
7.7%
O13181
 
7.2%
L11072
 
6.1%
S11035
 
6.0%
D8382
 
4.6%
J7903
 
4.3%
Other values (23)40038
21.9%
Common
ValueCountFrequency (%)
^8
23.5%
15
14.7%
~4
11.8%
'3
 
8.8%
|2
 
5.9%
*2
 
5.9%
»2
 
5.9%
"1
 
2.9%
(1
 
2.9%
«1
 
2.9%
Other values (5)5
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII181822
99.4%
Latin 1 Sup1078
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A26919
14.8%
N18223
10.0%
I16600
9.1%
E15397
 
8.5%
R14116
 
7.8%
O13181
 
7.2%
L11072
 
6.1%
S11035
 
6.1%
D8382
 
4.6%
J7903
 
4.3%
Other values (29)38994
21.4%
Latin 1 Sup
ValueCountFrequency (%)
É488
45.3%
Á357
33.1%
Í131
 
12.2%
Ú59
 
5.5%
Ó37
 
3.4%
Ñ2
 
0.2%
»2
 
0.2%
Ì1
 
0.1%
«1
 
0.1%

Perfil Riesgo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
2
13330 
3
10961 
4
4098 
1
2716 
5
 
629

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31734
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Length

2021-05-24T00:22:19.301997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:19.479567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Most occurring characters

ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31734
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common31734
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
213330
42.0%
310961
34.5%
44098
 
12.9%
12716
 
8.6%
5629
 
2.0%

Riesgo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Aventurero
13330 
Estratega
10961 
Planeador
4098 
Valiente
2716 
Cuidadoso
 
629

Length

Max length10
Median length9
Mean length9.334467763
Min length8

Characters and Unicode

Total characters296220
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValiente
2nd rowEstratega
3rd rowAventurero
4th rowAventurero
5th rowValiente

Common Values

ValueCountFrequency (%)
Aventurero13330
42.0%
Estratega10961
34.5%
Planeador4098
 
12.9%
Valiente2716
 
8.6%
Cuidadoso629
 
2.0%

Length

2021-05-24T00:22:19.967504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:20.158299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
aventurero13330
42.0%
estratega10961
34.5%
planeador4098
 
12.9%
valiente2716
 
8.6%
cuidadoso629
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e47151
15.9%
r41719
14.1%
t37968
12.8%
a33463
11.3%
n20144
6.8%
o18686
 
6.3%
u13959
 
4.7%
A13330
 
4.5%
v13330
 
4.5%
s11590
 
3.9%
Other values (8)44880
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter264486
89.3%
Uppercase Letter31734
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47151
17.8%
r41719
15.8%
t37968
14.4%
a33463
12.7%
n20144
7.6%
o18686
 
7.1%
u13959
 
5.3%
v13330
 
5.0%
s11590
 
4.4%
g10961
 
4.1%
Other values (3)15515
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
A13330
42.0%
E10961
34.5%
P4098
 
12.9%
V2716
 
8.6%
C629
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin296220
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47151
15.9%
r41719
14.1%
t37968
12.8%
a33463
11.3%
n20144
6.8%
o18686
 
6.3%
u13959
 
4.7%
A13330
 
4.5%
v13330
 
4.5%
s11590
 
3.9%
Other values (8)44880
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII296220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47151
15.9%
r41719
14.1%
t37968
12.8%
a33463
11.3%
n20144
6.8%
o18686
 
6.3%
u13959
 
4.7%
A13330
 
4.5%
v13330
 
4.5%
s11590
 
3.9%
Other values (8)44880
15.2%

Codigo CIIU
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct311
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.4177538
Minimum0
Maximum9820
Zeros20155
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:20.436672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile496
Maximum9820
Range9820
Interquartile range (IQR)10

Descriptive statistics

Standard deviation1253.606277
Coefficient of variation (CV)4.777139728
Kurtosis28.24183142
Mean262.4177538
Median Absolute Deviation (MAD)0
Skewness5.362802937
Sum8327565
Variance1571528.698
MonotonicityNot monotonic
2021-05-24T00:22:20.745793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020155
63.5%
105621
 
17.7%
901921
 
6.1%
811876
 
5.9%
496559
 
1.8%
497136
 
0.4%
620191
 
0.3%
177
 
0.2%
711040
 
0.1%
692039
 
0.1%
Other values (301)1219
 
3.8%
ValueCountFrequency (%)
020155
63.5%
177
 
0.2%
61
 
< 0.1%
105621
 
17.7%
201
 
< 0.1%
621
 
< 0.1%
671
 
< 0.1%
811876
 
5.9%
8238
 
0.1%
851
 
< 0.1%
ValueCountFrequency (%)
98201
 
< 0.1%
97003
 
< 0.1%
96096
< 0.1%
960210
< 0.1%
96011
 
< 0.1%
95215
< 0.1%
95123
 
< 0.1%
95119
< 0.1%
94121
 
< 0.1%
93291
 
< 0.1%

CIIU
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Bajo
24003 
Medio
5636 
Sin Clasificar
 
2002
Alto
 
93

Length

Max length14
Median length4
Mean length4.80847041
Min length4

Characters and Unicode

Total characters152592
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedio
2nd rowAlto
3rd rowSin Clasificar
4th rowAlto
5th rowAlto

Common Values

ValueCountFrequency (%)
Bajo24003
75.6%
Medio5636
 
17.8%
Sin Clasificar2002
 
6.3%
Alto93
 
0.3%

Length

2021-05-24T00:22:21.381260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:21.568595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bajo24003
71.1%
medio5636
 
16.7%
clasificar2002
 
5.9%
sin2002
 
5.9%
alto93
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o29732
19.5%
a28007
18.4%
B24003
15.7%
j24003
15.7%
i11642
 
7.6%
M5636
 
3.7%
e5636
 
3.7%
d5636
 
3.7%
l2095
 
1.4%
S2002
 
1.3%
Other values (9)14200
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter116854
76.6%
Uppercase Letter33736
 
22.1%
Space Separator2002
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o29732
25.4%
a28007
24.0%
j24003
20.5%
i11642
 
10.0%
e5636
 
4.8%
d5636
 
4.8%
l2095
 
1.8%
n2002
 
1.7%
s2002
 
1.7%
f2002
 
1.7%
Other values (3)4097
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
B24003
71.1%
M5636
 
16.7%
S2002
 
5.9%
C2002
 
5.9%
A93
 
0.3%
Space Separator
ValueCountFrequency (%)
2002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin150590
98.7%
Common2002
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o29732
19.7%
a28007
18.6%
B24003
15.9%
j24003
15.9%
i11642
 
7.7%
M5636
 
3.7%
e5636
 
3.7%
d5636
 
3.7%
l2095
 
1.4%
S2002
 
1.3%
Other values (8)12198
8.1%
Common
ValueCountFrequency (%)
2002
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII152592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o29732
19.5%
a28007
18.4%
B24003
15.7%
j24003
15.7%
i11642
 
7.6%
M5636
 
3.7%
e5636
 
3.7%
d5636
 
3.7%
l2095
 
1.4%
S2002
 
1.3%
Other values (9)14200
9.3%
Distinct10468
Distinct (%)33.0%
Missing1
Missing (%)< 0.1%
Memory size248.0 KiB
Minimum1934-07-30 00:00:00
Maximum2026-08-26 00:00:00
2021-05-24T00:22:21.799310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-24T00:22:22.151036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Cod_Ciudad_Nac.
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6849
Distinct (%)21.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean29376.87527
Minimum5001
Maximum229309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:22.908055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median25430
Q336386
95-th percentile76001
Maximum229309
Range224308
Interquartile range (IQR)25385

Descriptive statistics

Standard deviation23209.14872
Coefficient of variation (CV)0.790048244
Kurtosis4.002213499
Mean29376.87527
Median Absolute Deviation (MAD)14429
Skewness1.419535783
Sum932216383
Variance538664584.4
MonotonicityNot monotonic
2021-05-24T00:22:23.870113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110017047
 
22.2%
50011919
 
6.0%
760011145
 
3.6%
8001666
 
2.1%
12244627
 
2.0%
68001495
 
1.6%
13001367
 
1.2%
54001328
 
1.0%
17001309
 
1.0%
66001290
 
0.9%
Other values (6839)18540
58.4%
ValueCountFrequency (%)
50011919
6.0%
500217
 
0.1%
50041
 
< 0.1%
50215
 
< 0.1%
50308
 
< 0.1%
503110
 
< 0.1%
503419
 
0.1%
50361
 
< 0.1%
50382
 
< 0.1%
50405
 
< 0.1%
ValueCountFrequency (%)
2293091
 
< 0.1%
2098392
 
< 0.1%
2098321
 
< 0.1%
20975824
0.1%
2096921
 
< 0.1%
2096252
 
< 0.1%
2080941
 
< 0.1%
2079621
 
< 0.1%
2049911
 
< 0.1%
2041061
 
< 0.1%

Ciudad
Categorical

HIGH CARDINALITY

Distinct844
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Sin ciudad
10334 
BOGOTA D.C.
7047 
MEDELLIN
1919 
CALI
1145 
BARRANQUILLA
 
666
Other values (839)
10623 

Length

Max length34
Median length10
Mean length9.426640197
Min length3

Characters and Unicode

Total characters299145
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)0.5%

Sample

1st rowBOGOTA D.C.
2nd rowBOGOTA D.C.
3rd rowBOGOTA D.C.
4th rowBOGOTA D.C.
5th rowBOGOTA D.C.

Common Values

ValueCountFrequency (%)
Sin ciudad10334
32.6%
BOGOTA D.C.7047
22.2%
MEDELLIN1919
 
6.0%
CALI1145
 
3.6%
BARRANQUILLA666
 
2.1%
BUCARAMANGA495
 
1.6%
CARTAGENA367
 
1.2%
CUCUTA328
 
1.0%
MANIZALES309
 
1.0%
PEREIRA290
 
0.9%
Other values (834)8834
27.8%

Length

2021-05-24T00:22:26.633664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ciudad10348
20.1%
sin10334
20.1%
bogota7047
13.7%
d.c7047
13.7%
medellin1919
 
3.7%
cali1145
 
2.2%
barranquilla666
 
1.3%
bucaramanga495
 
1.0%
cartagena369
 
0.7%
de359
 
0.7%
Other values (845)11659
22.7%

Most occurring characters

ValueCountFrequency (%)
A28922
 
9.7%
i20668
 
6.9%
d20668
 
6.9%
19654
 
6.6%
O19377
 
6.5%
.14094
 
4.7%
S13717
 
4.6%
C12969
 
4.3%
T11072
 
3.7%
D10975
 
3.7%
Other values (29)127029
42.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter182665
61.1%
Lowercase Letter82672
27.6%
Space Separator19654
 
6.6%
Other Punctuation14094
 
4.7%
Open Punctuation30
 
< 0.1%
Close Punctuation30
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A28922
15.8%
O19377
10.6%
S13717
 
7.5%
C12969
 
7.1%
T11072
 
6.1%
D10975
 
6.0%
L10784
 
5.9%
I10425
 
5.7%
E10424
 
5.7%
B10016
 
5.5%
Other values (19)43984
24.1%
Lowercase Letter
ValueCountFrequency (%)
i20668
25.0%
d20668
25.0%
n10334
12.5%
c10334
12.5%
u10334
12.5%
a10334
12.5%
Space Separator
ValueCountFrequency (%)
19654
100.0%
Other Punctuation
ValueCountFrequency (%)
.14094
100.0%
Open Punctuation
ValueCountFrequency (%)
(30
100.0%
Close Punctuation
ValueCountFrequency (%)
)30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin265337
88.7%
Common33808
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A28922
 
10.9%
i20668
 
7.8%
d20668
 
7.8%
O19377
 
7.3%
S13717
 
5.2%
C12969
 
4.9%
T11072
 
4.2%
D10975
 
4.1%
L10784
 
4.1%
I10425
 
3.9%
Other values (25)105760
39.9%
Common
ValueCountFrequency (%)
19654
58.1%
.14094
41.7%
(30
 
0.1%
)30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII299035
> 99.9%
Latin 1 Sup110
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A28922
 
9.7%
i20668
 
6.9%
d20668
 
6.9%
19654
 
6.6%
O19377
 
6.5%
.14094
 
4.7%
S13717
 
4.6%
C12969
 
4.3%
T11072
 
3.7%
D10975
 
3.7%
Other values (25)126919
42.4%
Latin 1 Sup
ValueCountFrequency (%)
Ñ94
85.5%
Ú6
 
5.5%
Í6
 
5.5%
Ó4
 
3.6%

Cod Dpto.Nacimto.
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.82000378
Minimum0
Maximum99
Zeros797
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:28.132415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median11
Q352
95-th percentile76
Maximum99
Range99
Interquartile range (IQR)41

Descriptive statistics

Standard deviation26.34662842
Coefficient of variation (CV)0.9470389949
Kurtosis-0.7399373098
Mean27.82000378
Median Absolute Deviation (MAD)6
Skewness0.9425528167
Sum882840
Variance694.1448291
MonotonicityNot monotonic
2021-05-24T00:22:28.797576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
119928
31.3%
54578
14.4%
762640
 
8.3%
681402
 
4.4%
251334
 
4.2%
81136
 
3.6%
151003
 
3.2%
73833
 
2.6%
17801
 
2.5%
0797
 
2.5%
Other values (24)7282
22.9%
ValueCountFrequency (%)
0797
 
2.5%
54578
14.4%
81136
 
3.6%
119928
31.3%
13764
 
2.4%
151003
 
3.2%
17801
 
2.5%
18202
 
0.6%
19436
 
1.4%
20401
 
1.3%
ValueCountFrequency (%)
9912
 
< 0.1%
977
 
< 0.1%
9541
 
0.1%
9413
 
< 0.1%
9143
 
0.1%
8847
 
0.1%
86142
 
0.4%
85152
 
0.5%
81118
 
0.4%
762640
8.3%

Departamento
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
BOGOTA
9928 
ANTIOQUIA
4578 
VALLE DEL CAUCA
2640 
SANTANDER
1402 
CUNDINAMARCA
1334 
Other values (29)
11852 

Length

Max length18
Median length7
Mean length8.317987017
Min length4

Characters and Unicode

Total characters263963
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOGOTA
2nd rowBOGOTA
3rd rowBOGOTA
4th rowBOGOTA
5th rowBOGOTA

Common Values

ValueCountFrequency (%)
BOGOTA9928
31.3%
ANTIOQUIA4578
14.4%
VALLE DEL CAUCA2640
 
8.3%
SANTANDER1402
 
4.4%
CUNDINAMARCA1334
 
4.2%
ATLANTICO1136
 
3.6%
BOYACA1003
 
3.2%
TOLIMA833
 
2.6%
CALDAS801
 
2.5%
Sin departamento797
 
2.5%
Other values (24)7282
22.9%

Length

2021-05-24T00:22:30.034948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogota9928
25.1%
antioquia4578
11.6%
cauca3076
 
7.8%
valle2640
 
6.7%
del2640
 
6.7%
santander2143
 
5.4%
cundinamarca1334
 
3.4%
atlantico1136
 
2.9%
boyaca1003
 
2.5%
tolima833
 
2.1%
Other values (29)10237
25.9%

Most occurring characters

ValueCountFrequency (%)
A51460
19.5%
O31405
11.9%
T21327
 
8.1%
I15956
 
6.0%
N15065
 
5.7%
C13732
 
5.2%
L13187
 
5.0%
B12236
 
4.6%
U11071
 
4.2%
E10916
 
4.1%
Other values (24)67608
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter244991
92.8%
Lowercase Letter11158
 
4.2%
Space Separator7814
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A51460
21.0%
O31405
12.8%
T21327
8.7%
I15956
 
6.5%
N15065
 
6.1%
C13732
 
5.6%
L13187
 
5.4%
B12236
 
5.0%
U11071
 
4.5%
E10916
 
4.5%
Other values (13)48636
19.9%
Lowercase Letter
ValueCountFrequency (%)
n1594
14.3%
e1594
14.3%
a1594
14.3%
t1594
14.3%
i797
7.1%
d797
7.1%
p797
7.1%
r797
7.1%
m797
7.1%
o797
7.1%
Space Separator
ValueCountFrequency (%)
7814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin256149
97.0%
Common7814
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A51460
20.1%
O31405
12.3%
T21327
 
8.3%
I15956
 
6.2%
N15065
 
5.9%
C13732
 
5.4%
L13187
 
5.1%
B12236
 
4.8%
U11071
 
4.3%
E10916
 
4.3%
Other values (23)59794
23.3%
Common
ValueCountFrequency (%)
7814
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII263301
99.7%
Latin 1 Sup662
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A51460
19.5%
O31405
11.9%
T21327
 
8.1%
I15956
 
6.1%
N15065
 
5.7%
C13732
 
5.2%
L13187
 
5.0%
B12236
 
4.6%
U11071
 
4.2%
E10916
 
4.1%
Other values (23)66946
25.4%
Latin 1 Sup
ValueCountFrequency (%)
Ñ662
100.0%

Cod. Ocupacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7012352682
Minimum0
Maximum10
Zeros20158
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:30.310875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.012922081
Coefficient of variation (CV)1.444482511
Kurtosis-0.1457204395
Mean0.7012352682
Median Absolute Deviation (MAD)0
Skewness1.06447323
Sum22253
Variance1.026011142
MonotonicityNot monotonic
2021-05-24T00:22:30.611533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
020158
63.5%
26376
 
20.1%
13085
 
9.7%
32050
 
6.5%
464
 
0.2%
101
 
< 0.1%
ValueCountFrequency (%)
020158
63.5%
13085
 
9.7%
26376
 
20.1%
32050
 
6.5%
464
 
0.2%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
464
 
0.2%
32050
 
6.5%
26376
 
20.1%
13085
 
9.7%
020158
63.5%

Ocupacion
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
Codigo no esta Creado
20223 
EMPLEADO
6376 
INDEPENDIENTE
3085 
ESTUDIANTE
2050 

Length

Max length21
Median length21
Mean length16.899729
Min length8

Characters and Unicode

Total characters536296
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMPLEADO
2nd rowESTUDIANTE
3rd rowEMPLEADO
4th rowEMPLEADO
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
Codigo no esta Creado20223
63.7%
EMPLEADO6376
 
20.1%
INDEPENDIENTE3085
 
9.7%
ESTUDIANTE2050
 
6.5%

Length

2021-05-24T00:22:31.389629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:31.638826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
codigo20223
21.9%
creado20223
21.9%
no20223
21.9%
esta20223
21.9%
empleado6376
 
6.9%
independiente3085
 
3.3%
estudiante2050
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o80892
15.1%
60669
11.3%
C40446
 
7.5%
d40446
 
7.5%
e40446
 
7.5%
a40446
 
7.5%
E29192
 
5.4%
i20223
 
3.8%
g20223
 
3.8%
n20223
 
3.8%
Other values (14)143090
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter323568
60.3%
Uppercase Letter152059
28.4%
Space Separator60669
 
11.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C40446
26.6%
E29192
19.2%
D14596
 
9.6%
N11305
 
7.4%
P9461
 
6.2%
A8426
 
5.5%
I8220
 
5.4%
T7185
 
4.7%
M6376
 
4.2%
L6376
 
4.2%
Other values (3)10476
 
6.9%
Lowercase Letter
ValueCountFrequency (%)
o80892
25.0%
d40446
12.5%
e40446
12.5%
a40446
12.5%
i20223
 
6.2%
g20223
 
6.2%
n20223
 
6.2%
s20223
 
6.2%
t20223
 
6.2%
r20223
 
6.2%
Space Separator
ValueCountFrequency (%)
60669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin475627
88.7%
Common60669
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o80892
17.0%
C40446
 
8.5%
d40446
 
8.5%
e40446
 
8.5%
a40446
 
8.5%
E29192
 
6.1%
i20223
 
4.3%
g20223
 
4.3%
n20223
 
4.3%
s20223
 
4.3%
Other values (13)122867
25.8%
Common
ValueCountFrequency (%)
60669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII536296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o80892
15.1%
60669
11.3%
C40446
 
7.5%
d40446
 
7.5%
e40446
 
7.5%
a40446
 
7.5%
E29192
 
5.4%
i20223
 
3.8%
g20223
 
3.8%
n20223
 
3.8%
Other values (14)143090
26.7%

Ingresos_mensuales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct929
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4231418.131
Minimum0
Maximum1 × 1011
Zeros20157
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:32.249795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31000000
95-th percentile4500000
Maximum1 × 1011
Range1 × 1011
Interquartile range (IQR)1000000

Descriptive statistics

Standard deviation561578256
Coefficient of variation (CV)132.7163231
Kurtosis31682.20127
Mean4231418.131
Median Absolute Deviation (MAD)0
Skewness177.9249995
Sum1.34279823 × 1011
Variance3.153701377 × 1017
MonotonicityNot monotonic
2021-05-24T00:22:32.854966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020157
63.5%
10000001071
 
3.4%
2000000810
 
2.6%
1500000551
 
1.7%
800000507
 
1.6%
3000000492
 
1.6%
1200000462
 
1.5%
500000348
 
1.1%
2500000307
 
1.0%
5000000303
 
1.0%
Other values (919)6726
 
21.2%
ValueCountFrequency (%)
020157
63.5%
4001
 
< 0.1%
41021
 
< 0.1%
1000086
 
0.3%
120001
 
< 0.1%
150005
 
< 0.1%
170001
 
< 0.1%
2000033
 
0.1%
250005
 
< 0.1%
3000012
 
< 0.1%
ValueCountFrequency (%)
1 × 10111
< 0.1%
21514955501
< 0.1%
17700000001
< 0.1%
3453453451
< 0.1%
2310000001
< 0.1%
1500000001
< 0.1%
900000001
< 0.1%
850000001
< 0.1%
828116001
< 0.1%
700000001
< 0.1%

Egresos_mensuales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct389
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3801602.816
Minimum0
Maximum1.0000001 × 1011
Zeros20180
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:33.200715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3500000
95-th percentile2500000
Maximum1.0000001 × 1011
Range1.0000001 × 1011
Interquartile range (IQR)500000

Descriptive statistics

Standard deviation561438769.5
Coefficient of variation (CV)147.6847521
Kurtosis31714.2503
Mean3801602.816
Median Absolute Deviation (MAD)0
Skewness178.0577763
Sum1.206400638 × 1011
Variance3.152134919 × 1017
MonotonicityNot monotonic
2021-05-24T00:22:33.944547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020180
63.6%
1000000872
 
2.7%
500000840
 
2.6%
1500000640
 
2.0%
2000000629
 
2.0%
800000626
 
2.0%
600000609
 
1.9%
400000489
 
1.5%
700000444
 
1.4%
300000398
 
1.3%
Other values (379)6007
 
18.9%
ValueCountFrequency (%)
020180
63.6%
116
 
0.1%
21
 
< 0.1%
51
 
< 0.1%
106
 
< 0.1%
121
 
< 0.1%
201
 
< 0.1%
501
 
< 0.1%
601
 
< 0.1%
1003
 
< 0.1%
ValueCountFrequency (%)
1.0000001 × 10111
 
< 0.1%
12500000001
 
< 0.1%
8662992001
 
< 0.1%
6000004041
 
< 0.1%
6000000001
 
< 0.1%
1000000001
 
< 0.1%
400000001
 
< 0.1%
350000002
< 0.1%
330000001
 
< 0.1%
300000003
< 0.1%

Otros_Ing_Mensuales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct206
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101517.6671
Minimum0
Maximum258000000
Zeros26743
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:34.850141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile500000
Maximum258000000
Range258000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1670029.091
Coefficient of variation (CV)16.45062519
Kurtosis18056.76579
Mean101517.6671
Median Absolute Deviation (MAD)0
Skewness120.9799996
Sum3221561647
Variance2.788997166 × 1012
MonotonicityNot monotonic
2021-05-24T00:22:35.270541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026743
84.3%
100000734
 
2.3%
200000674
 
2.1%
500000484
 
1.5%
1000000338
 
1.1%
50000316
 
1.0%
300000283
 
0.9%
1211
 
0.7%
10000174
 
0.5%
400000149
 
0.5%
Other values (196)1628
 
5.1%
ValueCountFrequency (%)
026743
84.3%
1211
 
0.7%
22
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
1029
 
0.1%
121
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
2580000001
 
< 0.1%
600000001
 
< 0.1%
500000002
 
< 0.1%
450000001
 
< 0.1%
300000002
 
< 0.1%
263380001
 
< 0.1%
250000001
 
< 0.1%
200000006
< 0.1%
180000001
 
< 0.1%
150000002
 
< 0.1%

Total_Activos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct663
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22846094.16
Minimum0
Maximum1 × 1011
Zeros20176
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:35.645166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31000000
95-th percentile80000000
Maximum1 × 1011
Range1 × 1011
Interquartile range (IQR)1000000

Descriptive statistics

Standard deviation615480671.6
Coefficient of variation (CV)26.9403018
Kurtosis22502.69339
Mean22846094.16
Median Absolute Deviation (MAD)0
Skewness143.832213
Sum7.249979521 × 1011
Variance3.788164571 × 1017
MonotonicityNot monotonic
2021-05-24T00:22:36.155766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020176
63.6%
1000000930
 
2.9%
5000000626
 
2.0%
10000000622
 
2.0%
2000000553
 
1.7%
100000481
 
1.5%
500000383
 
1.2%
3000000348
 
1.1%
20000000342
 
1.1%
200000295
 
0.9%
Other values (653)6978
 
22.0%
ValueCountFrequency (%)
020176
63.6%
13
 
< 0.1%
101
 
< 0.1%
40001
 
< 0.1%
1000019
 
0.1%
101001
 
< 0.1%
110003
 
< 0.1%
130003
 
< 0.1%
130011
 
< 0.1%
140003
 
< 0.1%
ValueCountFrequency (%)
1 × 10111
 
< 0.1%
4 × 10101
 
< 0.1%
50000000003
< 0.1%
35590000001
 
< 0.1%
35000000001
 
< 0.1%
34350000001
 
< 0.1%
33978740001
 
< 0.1%
30000000002
< 0.1%
24000000001
 
< 0.1%
22298500001
 
< 0.1%

Total_Pasivos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct515
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7133180.942
Minimum0
Maximum1.740000003 × 1010
Zeros22580
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:36.717744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100000
95-th percentile30000000
Maximum1.740000003 × 1010
Range1.740000003 × 1010
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation106238882.9
Coefficient of variation (CV)14.89361952
Kurtosis22656.77968
Mean7133180.942
Median Absolute Deviation (MAD)0
Skewness139.5551282
Sum2.26364364 × 1011
Variance1.128670024 × 1016
MonotonicityNot monotonic
2021-05-24T00:22:37.042129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022580
71.2%
1000000571
 
1.8%
500000407
 
1.3%
100000389
 
1.2%
2000000361
 
1.1%
5000000334
 
1.1%
200000302
 
1.0%
3000000254
 
0.8%
10000000251
 
0.8%
300000242
 
0.8%
Other values (505)6043
 
19.0%
ValueCountFrequency (%)
022580
71.2%
1224
 
0.7%
25
 
< 0.1%
35
 
< 0.1%
41
 
< 0.1%
54
 
< 0.1%
61
 
< 0.1%
1028
 
0.1%
111
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
1.740000003 × 10101
< 0.1%
20000008261
< 0.1%
20000000001
< 0.1%
19500000001
< 0.1%
18000000001
< 0.1%
13000000001
< 0.1%
11879900001
< 0.1%
10000000081
< 0.1%
10000000002
< 0.1%
9000000001
< 0.1%

Saldo
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct7948
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157603.5221
Minimum0
Maximum231883698.8
Zeros22013
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:37.345716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310631.5675
95-th percentile318416.8165
Maximum231883698.8
Range231883698.8
Interquartile range (IQR)10631.5675

Descriptive statistics

Standard deviation2115046.623
Coefficient of variation (CV)13.42004668
Kurtosis6162.268949
Mean157603.5221
Median Absolute Deviation (MAD)0
Skewness66.44705337
Sum5001390171
Variance4.473422219 × 1012
MonotonicityNot monotonic
2021-05-24T00:22:37.625273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022013
69.4%
47842.85269
 
0.8%
28705.18143
 
0.5%
95685.1952
 
0.2%
47841.6529
 
0.1%
19118.6313
 
< 0.1%
10556.6713
 
< 0.1%
9883.811
 
< 0.1%
10535.8411
 
< 0.1%
21114.4710
 
< 0.1%
Other values (7938)9170
28.9%
ValueCountFrequency (%)
022013
69.4%
761.691
 
< 0.1%
1544.011
 
< 0.1%
2073.391
 
< 0.1%
3222.721
 
< 0.1%
3902.741
 
< 0.1%
4038.521
 
< 0.1%
4285.21
 
< 0.1%
5560.611
 
< 0.1%
5683.681
 
< 0.1%
ValueCountFrequency (%)
231883698.81
< 0.1%
172734749.81
< 0.1%
88879567.651
< 0.1%
76269924.111
< 0.1%
59572485.761
< 0.1%
54304557.641
< 0.1%
49085033.091
< 0.1%
48625237.631
< 0.1%
43102468.911
< 0.1%
35024793.131
< 0.1%

Transacciones
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.781338627
Minimum0
Maximum82
Zeros12588
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:37.924567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum82
Range82
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.896140971
Coefficient of variation (CV)1.760354141
Kurtosis31.74335327
Mean2.781338627
Median Absolute Deviation (MAD)1
Skewness4.480378013
Sum88263
Variance23.97219641
MonotonicityNot monotonic
2021-05-24T00:22:38.981345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012588
39.7%
25184
16.3%
14182
 
13.2%
52013
 
6.3%
31900
 
6.0%
61251
 
3.9%
41130
 
3.6%
7711
 
2.2%
8524
 
1.7%
9377
 
1.2%
Other values (56)1874
 
5.9%
ValueCountFrequency (%)
012588
39.7%
14182
 
13.2%
25184
16.3%
31900
 
6.0%
41130
 
3.6%
52013
 
6.3%
61251
 
3.9%
7711
 
2.2%
8524
 
1.7%
9377
 
1.2%
ValueCountFrequency (%)
821
< 0.1%
732
< 0.1%
671
< 0.1%
661
< 0.1%
632
< 0.1%
611
< 0.1%
601
< 0.1%
582
< 0.1%
571
< 0.1%
561
< 0.1%

Transacciones Adiciones
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.87965589
Minimum0
Maximum77
Zeros12593
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:39.391156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum77
Range77
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.729794485
Coefficient of variation (CV)1.984296437
Kurtosis46.48523195
Mean1.87965589
Median Absolute Deviation (MAD)1
Skewness5.477907699
Sum59649
Variance13.9113669
MonotonicityNot monotonic
2021-05-24T00:22:39.724472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012593
39.7%
19399
29.6%
33146
 
9.9%
22450
 
7.7%
41151
 
3.6%
5655
 
2.1%
6450
 
1.4%
7315
 
1.0%
8253
 
0.8%
9183
 
0.6%
Other values (50)1139
 
3.6%
ValueCountFrequency (%)
012593
39.7%
19399
29.6%
22450
 
7.7%
33146
 
9.9%
41151
 
3.6%
5655
 
2.1%
6450
 
1.4%
7315
 
1.0%
8253
 
0.8%
9183
 
0.6%
ValueCountFrequency (%)
771
< 0.1%
611
< 0.1%
591
< 0.1%
571
< 0.1%
561
< 0.1%
551
< 0.1%
541
< 0.1%
531
< 0.1%
521
< 0.1%
511
< 0.1%

Transacciones Retiros
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9016827378
Minimum0
Maximum35
Zeros18546
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size248.0 KiB
2021-05-24T00:22:40.060467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum35
Range35
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.645622084
Coefficient of variation (CV)1.825056658
Kurtosis37.82531476
Mean0.9016827378
Median Absolute Deviation (MAD)0
Skewness4.361102423
Sum28614
Variance2.708072043
MonotonicityNot monotonic
2021-05-24T00:22:40.971087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
018546
58.4%
16306
 
19.9%
23310
 
10.4%
31855
 
5.8%
4725
 
2.3%
5347
 
1.1%
6249
 
0.8%
8104
 
0.3%
783
 
0.3%
956
 
0.2%
Other values (19)153
 
0.5%
ValueCountFrequency (%)
018546
58.4%
16306
 
19.9%
23310
 
10.4%
31855
 
5.8%
4725
 
2.3%
5347
 
1.1%
6249
 
0.8%
783
 
0.3%
8104
 
0.3%
956
 
0.2%
ValueCountFrequency (%)
351
< 0.1%
341
< 0.1%
271
< 0.1%
261
< 0.1%
251
< 0.1%
231
< 0.1%
222
< 0.1%
212
< 0.1%
202
< 0.1%
192
< 0.1%

cant
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size248.0 KiB
1
31734 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31734
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
131734
100.0%

Length

2021-05-24T00:22:41.471073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-24T00:22:41.619920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
131734
100.0%

Most occurring characters

ValueCountFrequency (%)
131734
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31734
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common31734
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131734
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131734
100.0%

Interactions

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2021-05-24T00:22:08.930362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-24T00:22:41.783406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-24T00:22:42.556301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-24T00:22:43.090010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-24T00:22:43.656664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-24T00:22:44.093449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-24T00:22:10.613344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-24T00:22:13.912976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-24T00:22:14.980959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-24T00:22:15.342689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Fecha RegistroId ModificadoTipo ClientePrimer NombrePerfil RiesgoRiesgoCodigo CIIUCIIUFecha de NacimientoCod_Ciudad_Nac.CiudadCod Dpto.Nacimto.DepartamentoCod. OcupacionOcupacionIngresos_mensualesEgresos_mensualesOtros_Ing_MensualesTotal_ActivosTotal_PasivosSaldoTransaccionesTransacciones AdicionesTransacciones Retiroscant
02017-08-241Ualet puroDANIEL1Valiente10Medio1991-11-1211001.0BOGOTA D.C.11BOGOTA2EMPLEADO530000015000000340000000400000000.008621
12017-08-242Ualet puroJESSICA3Estratega112Alto1992-05-2511001.0BOGOTA D.C.11BOGOTA3ESTUDIANTE2588888555558888888000.002111
22017-08-243Ualet puroDAVID2Aventurero7210Sin Clasificar1995-06-2211001.0BOGOTA D.C.11BOGOTA2EMPLEADO737000650000012000000070000000.000001
32017-08-244Ualet puroDIEGO2Aventurero1Alto1988-05-0111001.0BOGOTA D.C.11BOGOTA2EMPLEADO513400048000000100000000800000000.000001
42017-08-245Ualet puroNICOLÁS1Valiente1Alto1995-05-0511001.0BOGOTA D.C.11BOGOTA3ESTUDIANTE7000000100000000.006511
52017-08-246Ualet puroJUAN1Valiente1Alto1992-08-136183.0Sin ciudad5ANTIOQUIA2EMPLEADO50000003000000010000000020000000.004311
62017-08-247Ualet puroDIANA2Aventurero1Alto1986-03-286249.0Sin ciudad8ATLANTICO2EMPLEADO14500000900000001000000001500000064740.478621
72017-08-248Ualet puroCAMILO1Valiente1Alto1991-04-027182.0Sin ciudad76VALLE DEL CAUCA1INDEPENDIENTE1200000030000000196035000519360000.002111
82017-08-249Ualet puroOMAR4Planeador0Bajo1963-10-0673283.0FRESNO73TOLIMA0Codigo no esta Creado000003199917.17352961
92017-08-2410Ualet puroCELSO4Planeador10Medio1964-01-126705.0Sin ciudad25CUNDINAMARCA2EMPLEADO190000001000000006000000002000000001136146.439721

Last rows

Fecha RegistroId ModificadoTipo ClientePrimer NombrePerfil RiesgoRiesgoCodigo CIIUCIIUFecha de NacimientoCod_Ciudad_Nac.CiudadCod Dpto.Nacimto.DepartamentoCod. OcupacionOcupacionIngresos_mensualesEgresos_mensualesOtros_Ing_MensualesTotal_ActivosTotal_PasivosSaldoTransaccionesTransacciones AdicionesTransacciones Retiroscant
317242021-03-2531845Ualet puroNATALIA4Planeador0Bajo1978-12-0536576.0Sin ciudad11BOGOTA0Codigo no esta Creado000000.00001
317252021-03-2531846Ualet puroJONATAN3Estratega0Bajo1993-01-1730789.0Sin ciudad11BOGOTA0Codigo no esta Creado000000.00001
317262021-03-2531847Ualet puroIVAN3Estratega0Bajo1986-05-2732767.0Sin ciudad11BOGOTA0Codigo no esta Creado000000.00001
317272021-03-2531848Ualet puroDAVID4Planeador0Bajo1978-11-0236494.0Sin ciudad15BOYACA0Codigo no esta Creado000000.00001
317282021-03-2531849Ualet puroANGIE3Estratega0Bajo1996-03-1036522.0Sin ciudad15BOYACA0Codigo no esta Creado000000.00001
317292021-03-2531850Ualet puroDIEGO4Planeador0Bajo2000-03-1633611.0Sin ciudad11BOGOTA0Codigo no esta Creado000000.00001
317302021-03-2531851Ualet puroDIEGO4Planeador0Bajo1976-05-0524162.0Sin ciudad13BOLIVAR0Codigo no esta Creado000000.00001
317312021-03-2531852Ualet puroBRANDON4Planeador0Bajo1955-03-2035629.0Sin ciudad11BOGOTA0Codigo no esta Creado000000.00001
317322021-03-2531853Ualet puroSANTIAGO4Planeador0Bajo1991-12-1436470.0Sin ciudad68SANTANDER0Codigo no esta Creado000000.00001
317332021-03-2531854Ualet puroCARLOS4Planeador0Bajo1995-08-2534913.0Sin ciudad5ANTIOQUIA0Codigo no esta Creado000000.00001